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DEVASSYJP Aug 21, 2024

Transformer-based language models like BERT, T5, GPT, DALL-E, and ChatGPT have revolutionized natural language processing (NLP) by outperforming traditional machine learning methods in complex natural language understanding (NLU) tasks. This book explores the power of Transformers beyond NLP, diving into the rapidly growing fields of multimodal learning and generative Al with impressive results.Readers will learn to implement multimodal solutions, including text-to-image generation, and will understand the fundamentals of various transformer models. The book also covers training autoregressive language models like GPT and XLNet, boosting model performance, and tracking model training using TensorBoard. Later chapters focus on using vision transformers for computer vision problems and applying transformers to model time series data and predictions.By the end, readers will have a strong grasp of transformer models and their applications in solving challenges across NLP and computer vision. This book is an invaluable resource for those looking to enhance their expertise in thesecutting-edge technologies.

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Rohan Pandit Jun 09, 2024

Transformer-based language models like BERT, T5, GPT, DALL-E, and ChatGPT have revolutionized natural language processing (NLP) by outperforming traditional machine learning methods in complex natural language understanding (NLU) tasks. This book explores the power of Transformers beyond NLP, diving into the rapidly growing fields of multimodal learning and generative AI with impressive results.Readers will learn to implement multimodal solutions, including text-to-image generation, and will understand the fundamentals of various transformer models. The book also covers training autoregressive language models like GPT and XLNet, boosting model performance, and tracking model training using TensorBoard. Later chapters focus on using vision transformers for computer vision problems and applying transformers to model time series data and predictions.By the end, readers will have a strong grasp of transformer models and their applications in solving challenges across NLP and computer vision. This book is an invaluable resource for those looking to enhance their expertise in these cutting-edge technologies.

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Soni Raju Jun 26, 2024

This book explores what readers will learn to implement multimodal solutions, including text-to-image generation, and will understand the fundamentals of various transformer models. The book also covers training autoregressive language models like GPT and XLNet, boosting model performance, and tracking model training using TensorBoard. In the end, readers will have a strong grasp of transformer models and their applications in solving challenges across NLP and computer vision. This book is an invaluable resource.

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The fan is incredible with 4 different variations of air speed. Perfect for outdoors. You can just clip it on shirt or pants and you are good to go. Loved it Aug 14, 2024

This book goes through the evolution of Natural Language Processing (NLP), from traditional methods to the cutting-edge Transformer architecture. It serves as an invaluable resource for professionals and enthusiasts in machine learning, deep learning, and NLP.Key Highlights:1. From Basics to Advanced: The book starts with the foundational concepts, making it accessible for beginners, and progresses to advanced topics, ensuring that even seasoned professionals find value.2. Hands-On Approach: The authors provide practical examples and code snippets, allowing readers to experiment and implement concepts in real-time.3. Comprehensive Coverage: Topics like autoencoding language models, generative models, fine-tuning for various tasks, and the latest advancements in large language models are covered in-depth.4. Multimodal Learning: The book explores the exciting realm of multimodal learning, discussing how Transformers can be used beyond NLP, such as in computer vision and generative AI.5. Future-Proofing: With a detailed look at efficient Transformers and parameter-efficient fine-tuning, the book prepares you for the future of scalable and sustainable AI models.Whether you're a researcher, practitioner, or educator in the AI field, this book is a must-read to stay ahead in the rapidly evolving landscape of NLP and Transformers.

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Banachan Aug 26, 2024

I have the 1st edition of the book and this 2nd edition is a great update to the first one to keep up with the times. If you enjoyed the 1st ed, then you don't want to miss this second update. Lots of new information given the new and rapid developments the LLM space has gone through. It continues to be a great comprehensive guide to understanding and implementing Transformer models in various artificial intelligence (AI) tasks. It provides a deep dive into the architecture of Transformers, showcasing their effectiveness across a range of areas including Natural Language Processing (NLP), computer vision, time series analysis, and multimodal tasks.It goes more into details on how Transformers is applied to time series data, covering the fundamental concepts of time series and demonstrating how it can be effectively utilized for these types of data, which many models struggle with due to their complexity. It goes into the ubiquity and superiority in handling diverse tasks compared to other architectures and their pivotal role in the rise of Generative AI within both industry and academic communities. A fun read at that.It has quite a comprehensive coverage, providing an in-depth look at the theory and application across different domains, making it a valuable resource for those interested in understanding various aspects of this powerful model architecture. There are ample practical examples through step-by-step guidance which aids in the understanding and implementation of concepts discussed. Some cutting-edge Content to keep up with the times, like current and emerging trends in generative tasks and multimodal learning.There are recent topics in this field that I thought would have been nice if included, like topics on ethics on Gen AI, deployment scenarios, emergence of small language models and what it means, how RLHF plays more into the area, how it can work with quantum computing, etc. While I am a proponent of deep dives, beginners to transformers might not get the more generalized explanations from scratch. I figured it can't be all things to all people. But an overall great read.

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Mastering Transformers: The Journey from BERT to Large Language Models and Stable Diffusion , Second Edition

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